Data security and privacy are a big concern for companies and individuals alike. With all of the data breaches that have happened in recent years, it’s no wonder that people are looking for ways to protect their information. As a result, the global cybersecurity market was valued at $156.24 billion in 2020.
Homomorphic encryption is a kind of cryptography that allows mathematical operations to be performed on ciphertext without needing to decrypt it first. You can learn more about homomorphic encryption at
https://tripleblind.ai/homomorphic-encryption-for-data-privacy/.
What is Homomorphic Encryption?
Homomorphic encryption allows for the computation of encrypted data. It means that data can be processed and analyzed without first having to decrypt it.
Homomorphic encryption is a relatively new concept and is still being developed. However, it has the potential to be extremely powerful, particularly in terms of privacy and security. Data encryption is a security idea that encodes information and cannot be decrypted or accessed unless the user has the correct encryption key.
How Does Homomorphic Encryption Work
Here’s how it works: First, you must encrypt your data. It is done by turning your data into a mathematical formula. Then, you can do whatever math you want to the encrypted data, which will still be encrypted. Finally, you decrypt the results of your math equation. You’ve successfully used homomorphic encryption.
A recent Ponemon Institute study found that organizations spend $3.86 million (about €3.4 million) recovering from cyberattacks. However, with the help of homomorphic encryption, businesses can keep their data safe and avoid these costly breaches.
Homomorphic encryption is a powerful tool that can help businesses protect their data.
Types of Homomorphic Encryption
There are three types of homomorphic encryption
1) Partially Homomorphic Encryption (PHE): When you can perform only certain types of operations on encrypted data. For instance, you can add two ciphertexts but cannot multiply them.
RSA is an example of a PHE scheme.
Partial homomorphic encryption (PHE) schemes allow for more than one type of operation on the ciphertext, but not all operations. For instance, the Paillier scheme allows for both addition and multiplication of ciphertexts, not division.
2) Somewhat Homomorphic Encryption (SHE): If you want to perform more than one operation on the ciphertext, then you need to use SHE. With SHE, you can do a limited number of computations on encrypted data without decrypting it. However, the number of computations that can be done is determined by the level of noise in the system, which limits the security.
Some benefits of SHE include:
-The data owner can outsource the computation to untrusted third parties without worrying about information leakage.
-It can be used for private query services.
-It is possible to do real-time analysis of encrypted data.
SHE schemes have been developed for both symmetric and asymmetric key settings.
3) Fully Homomorphic Encryption (FHE)
With FHE, you can perform unlimited computations on ciphertexts, including decryption, without ever decrypting them. It allows complete secure computation outsourcing.
Some benefits of FHE are:
-The data is always encrypted, so it’s more secure.
-Data doesn’t need to be decrypted before processing, which can be a security risk.
-FHE can be used for many computations, including machine learning and AI.
Conclusion
Whether you’re a data scientist, engineer, or business executive, it’s important to have a basic understanding of homomorphic encryption and its potential implications for the future of data privacy.
